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1 Financial Inclusion: New Measurement and Cross-Country ImpACt Assessment Cyn-Young Park and Rogelio V. Mercado, Jr. NO. 539 March 2018 adb economics working paper series

2 ADB Economics Working Paper Series Financial Inclusion: New Measurement and Cross-Country Impact Assessment Cyn-Young Park and Rogelio V. Mercado, Jr. No. 539 March 2018 Cyn-Young Park is Director in the Economic Research and Regional Cooperation Department of the Asian Development Bank. Rogelio V. Mercado, Jr. is Senior Economist in the Macroeconomics and Monetary Policy Division of The South East Asian Central Banks Research and Training Centre. An earlier version of this draft was presented at the Financial Development and Inclusion Workshop hosted by the Centre for Applied Finance and Economics of the University of South Australia on 1 2 December 2017; and released as South East Asian Central Banks Research and Training Centre Working Paper 29/2018. The authors thank the discussant and participants for comments and suggestions which have been incorporated in this paper. The views expressed in this paper are those of the authors and do not necessarily reflect the views and policies of the Asian Development Bank or its Board of Governors or the governments they represent; as well as those of the South East Asian Central Banks Research and Training Centre or its member central banks/monetary authorities. ASIAN DEVELOPMENT BANK

3 Creative Commons Attribution 3.0 IGO license (CC BY 3.0 IGO) 2018 Asian Development Bank 6 ADB Avenue, Mandaluyong City, 1550 Metro Manila, Philippines Tel ; Fax Some rights reserved. Published in ISSN (print), (electronic) Publication Stock No. WPS DOI: The views expressed in this publication are those of the authors and do not necessarily reflect the views and policies of the Asian Development Bank (ADB) or its Board of Governors or the governments they represent. ADB does not guarantee the accuracy of the data included in this publication and accepts no responsibility for any consequence of their use. The mention of specific companies or products of manufacturers does not imply that they are endorsed or recommended by ADB in preference to others of a similar nature that are not mentioned. By making any designation of or reference to a particular territory or geographic area, or by using the term country in this document, ADB does not intend to make any judgments as to the legal or other status of any territory or area. This work is available under the Creative Commons Attribution 3.0 IGO license (CC BY 3.0 IGO) By using the content of this publication, you agree to be bound by the terms of this license. For attribution, translations, adaptations, and permissions, please read the provisions and terms of use at This CC license does not apply to non-adb copyright materials in this publication. If the material is attributed to another source, please contact the copyright owner or publisher of that source for permission to reproduce it. ADB cannot be held liable for any claims that arise as a result of your use of the material. Please contact pubsmarketing@adb.org if you have questions or comments with respect to content, or if you wish to obtain copyright permission for your intended use that does not fall within these terms, or for permission to use the ADB logo. Notes: In this publication, $ refers to US dollars. Corrigenda to ADB publications may be found at

4 CONTENTS TABLES AND FIGURES ABSTRACT iv v I. INTRODUCTION 1 II. FINANCIAL INCLUSION CONCEPTS AND MEASUREMENT 2 III. INDEX OF FINANCIAL INCLUSION 4 IV. EMPIRICAL METHODOLOGY AND RESULTS 8 V. CONCLUSIONS 23 APPENDIX 25 REFERENCES 27

5 TABLES AND FIGURES TABLES 1 Principal Component Analysis of Indicators for Each Index 7 of Financial Inclusion Dimension 2 Index of Financial Inclusion Ranking, Estimates on the Change in Index of Financial Inclusion, by Income Group 17 4 Estimates on Poverty, Income Inequality, Entrepreneurship, and 18 Female Empowerment, Full Sample 5 Estimates on Poverty, Income Inequality, Entrepreneurship, and 18 Female Empowerment, High- and Middle-High-Income Economies 6 Estimates on Poverty, Income Inequality, Entrepreneurship, and 19 Female Empowerment, Middle-Low and Low-Income Economies 7a Estimates on Poverty, Income Inequality, Entrepreneurship, and 20 Female Empowerment, with High-Income Interaction Effects 7b Estimates on Poverty, Income Inequality, Entrepreneurship, and 20 Female Empowerment, with Middle-High-Income Interaction Effects 7c Estimates on Poverty, Income Inequality, Entrepreneurship, and 21 Female Empowerment, with Middle-Low-Income Interaction Effects 7d Estimates on Poverty, Income Inequality, Entrepreneurship, and 21 Female Empowerment, with Low-Income Interaction Effects 8 Estimates on Poverty, Income Inequality, Entrepreneurship, and 23 Female Empowerment, with Rule of Law FIGURES 1 Index of Financial Inclusion and Sarma Measure (2015) 12 2 Index of Financial Inclusion by Income Group (Median) 13 3 Change in Financial Inclusion on Gross Domestic Product Growth 14 4 Poverty on Index of Financial Inclusion 14 5 Income Inequality on Index of Financial Inclusion 15 6 Rule of Law on Index of Financial Inclusion 16

6 ABSTRACT This paper introduces a new index of financial inclusion for 151 economies using principal component analysis to compute weights for aggregating nine indicators of access, availability, and usage. It then assesses the impact of financial inclusion on poverty and income inequality. The results provide evidence that high- and middle-high-income economies with high financial inclusion have significantly lower poverty, while no such relation exists for middle-low and low-income economies. The nonlinearities in the cross-country determinants and impacts of financial inclusion on poverty and income inequality across income groups are important to choosing the appropriate policies for achieving inclusive growth in different development stages. Key words: financial inclusion, income inequality, poverty JEL codes: G18, O11, O16

7 I. INTRODUCTION Financial inclusion aids inclusive growth, economic development, and financial deepening. More practically, it can increase poor people s access to financial services, reducing poverty and lowering income inequality. The empirical evidence supports this view. Indeed, simply having a bank account increases savings, empowers women, boosts household consumption, and raises productive investment (Allen et al. 2012; Beck, Demirgüç-Kunt, and Honohan 2009). As such, policy makers around the world have pursued financial inclusion as a major policy goal, with G20 leaders recognizing it as one of the main pillars of the global development agenda. Recent policy initiatives vary in scope and purpose. For instance, the World Bank has recently made available the Global Financial Inclusion (Global Findex) database to measure and track the progress of financial inclusion across member countries. And to help improve financial inclusion policy in developing and emerging market economies, the Alliance for Financial Inclusion was established in 2008 as a network of financial inclusion policy makers. It would now be useful, with these and other initiatives in place, to have a tool to track progress, assess impact, identify challenges, and suggest policy direction. The literature on financial inclusion falls broadly into two categories. The first strand considers individual and household impacts and determinants of financial inclusion using field experiments. Burgess and Pande (2005), for example, report that state-led expansion of rural bank branches in India has helped reduce poverty. Specifically, the authors find robust evidence that opening bank branches in rural unbanked locations in India is associated with lower poverty in those areas. Similarly, Brune et al. (2011) show that increased financial access through commitmentsavings accounts in rural Malawi improves the well-being of poor households, which were able to keep their savings for agricultural inputs, creating an access to funds for lean periods. Allen et al. (2013) illustrate that by tapping underprivileged households, commercial banks can help improve the financial access of the poor in Kenya. The second strand focuses more on cross-country aggregate trends and impacts of financial inclusion. Honohan (2008) finds that a set of country-specific structural variables matter for financial access. For example, more aid as percent of gross domestic product (GDP), higher age-dependency ratio, and higher population density significantly reduce financial access; while more mobile phone subscriptions and higher quality of institutions significantly increase financial access. Aid dependency suggests more poverty and age dependency implies more children who many not have access to financial services. Negative correlation between population density and financial access is rather counterintuitive, but its significance disappears when the two largest outliers, Hong Kong, China and Lebanon, are excluded from the data set. Looking at the cross-country link between poverty and financial access, his results show that financial access significantly reduces poverty, but the result is valid only when financial access is the sole regressor. In an earlier version of his paper, Honohan (2007) tested the significance of financial access in reducing income equality. His results show that higher financial access significantly reduces income inequality. However, the link between the two variables depends on which specification is used. Rojas-Suarez (2010) used the same indicator constructed by Honohan (2008) to test the significance of various macroeconomic and country characteristics for financial access among a group of emerging economies. The results show that economic volatility, weak rule of law, higher income inequality, and social underdevelopment and regulatory constraints significantly lower financial inclusion. Park and Mercado (2016, 2018) later confirmed these earlier findings, showing that per

8 2 ADB Economics Working Paper Series No. 539 capita income, rule of law, and demographic characteristics are significantly positively correlated with financial inclusion for both global and Asian samples. They also find that financial inclusion is significantly correlated with lower poverty for both global and developing Asia samples. Although their results point to a significant covariation between income inequality and financial inclusion in their full sample, no such covariance is found in the developing Asia sample. Both strands of empirical literature are equally relevant to policy making. While the experimental literature for financial inclusion is growing rapidly, with new papers focusing on more specific evidence from randomized control trials or quasi-randomized impact evaluations, macroeconomic level studies use country panel data comparisons to establish the general relationship between financial inclusion and economic growth/employment. This paper follows the second approach by investigating the aggregate impact of financial inclusion on overall poverty, income inequality, entrepreneurship, and female empowerment. We construct a new index of financial inclusion (IFI) for 151 economies with indicators based on the World Bank s Global Findex database to assess cross-country variation in the impact of financial inclusion on key development objectives. We ask two questions. First, what factors are relevant in explaining cross-country differences in the recent change in financial inclusion? Second, does financial inclusion lower poverty and income inequality, and improve entrepreneurship and female empowerment? This paper contributes to the literature in several ways. First, the new financial inclusion measure combines Sarma s (2008) multidimension approach with the normalized weights from the principal component analysis of Camara and Tuesta (2014) to address the well-known weaknesses of each methodology. The new index shows that the indicators and dimension weights from the principal component analysis are relatively stable between two survey periods, 2011 and Second, our estimates provide robust evidence using best available cross-country data that economies with high financial inclusion have significantly lower poverty rates. This validates the causal inverse relation between financial access and poverty at the individual and household level on a crosscountry setting. Third, splitting the sample by country income groups, we find higher financial inclusion significantly covaries with higher output growth and lower poverty rates for high and middle-highincome economies. However, for middle-low and low-income economies, these significant relationships lose their significance. This suggests that there may be nonlinearities in country-specific factors that may influence the relationship between financial inclusion and economic growth/poverty. The paper proceeds as follows. Section II discusses conceptual and measurement issues on financial inclusion. Section III explains the methodology in constructing a new financial inclusion index and provides stylized facts. Section IV presents the empirical approach and discusses the results. Section V concludes. II. FINANCIAL INCLUSION CONCEPTS AND MEASUREMENT Definitions of financial inclusion vary. Several studies define the concept within the broader context of social inclusion. For example, Leyshon and Thrift (1995) highlight the exclusion of some groups and individuals from access to formal financial systems, while Sinclair (2001) focuses on the inability to access necessary financial services in an appropriate form. Amidžić, Massara, and Mialou (2014) and

9 Financial Inclusion: New Measurement and Cross-Country Impact Assessment 3 Sarma (2008) directly define financial inclusion as an economic state in which individuals and firms are not denied access to basic financial services. Sarma (2008) defines financial inclusion as a process that ensures ease of access, availability, and usage of formal financial systems for all members of an economy. In contrast, Camara and Tuesta (2014) define an inclusive financial system as one that maximizes usage and access while minimizing involuntary exclusions. Hence, they focus more on usage, access, and barriers, which capture both the supply- and demand-side of financial access. It is also important to distinguish between voluntary and involuntary financial exclusion. The World Bank (2014) defines voluntary exclusion as a condition in which a segment of the population or firms chooses not to use financial services, either because they have no need for them or for cultural or religious reasons. In contrast, involuntary exclusion arises from insufficient income, a high-risk profile due to discrimination, and financial market failures and imperfections. This involuntary element, which is viewed as a barrier to financial inclusion, requires policy and research initiatives, as it can be addressed with appropriate economic programs and policies to increase income and correct market failures and imperfections. This paper follows the definition of financial inclusion of Sarma (2008), who views it as a process that ensures ease of access, availability, and usage of financial services for all members of society. The advantage in this definition is that it builds the concept of financial inclusion based on several dimensions, including accessibility, availability, and usage, which can be assessed separately. More importantly, Sarma (2008) strictly delineates financial inclusion dimensions focusing on the financial access of a segment of the population included in the financial system. Defining financial inclusion to include barriers or ease of financial access, a la Camara and Tuesta (2014), confuses the conceptual clarity of financial inclusion, as it combines the reasons for having and not having financial access in a financial inclusion measure. 1 And just as no single conceptual definition of financial inclusion exists, no standard measure of the concept is universally accepted. Consequently, measures of financial inclusion often vary across studies. For instance, Honohan (2007, 2008) constructed a financial access indicator that captures the fraction of the adult population in each economy with access to formal financial intermediaries which captures only one dimension of financial inclusion. This composite financial access indicator was constructed using household survey data for economies with available data on financial access. For those without a household survey on financial access, the indicator was derived using information on bank account numbers and GDP per capita. The data set was constructed as a cross-section series using the most recent data as the reference year, which varies across economies. Amidžić, Massara, and Mialou (2014) constructed a financial inclusion indicator as a composite of variables pertaining to multiple dimensions: outreach (geographic and demographic penetration); usage (deposit and lending); and quality (disclosure requirement, dispute resolution, and cost of usage). 2 Each measure is normalized, statistically identified for each dimension, and then aggregated using statistical weights, the aggregation following a weighted geometric mean. One 1 2 Camara and Tuesta (2014) argued that barriers to financial access must be included as a dimension of financial inclusion as they reflect demand-side measures of financial services. However, demand-side indicators could also be included in a multidimensional approach of Sarma (2008). In other words, the lack of demand-side measures in existing financial inclusion measures does not fully justify the inclusion of barriers dimension in the aggregate financial inclusion measure. Although Amidžić, Massara, and Mialou (2014) defined proxies for a quality measure, they did not include it in their composite indicator due to a lack of reliable and available data.

10 4 ADB Economics Working Paper Series No. 539 drawback of this approach is that it uses a factor analysis method to reduce a set of variables down to a smaller number of factors and, therefore, not fully utilizing all available data for each country. Furthermore, it assigns different weights to each dimension, which may imply the importance of one dimension over another. Sarma (2008) followed a different approach to construct the indicator. She first computed a subindex for each dimension of financial inclusion (access, availability, and usage) and then aggregated each index as the normalized inverse of Euclidean distance, where the distance is computed from a reference ideal point and then normalized by the number of dimensions included in the aggregate index. The advantage of this approach is that it is easy to compute and does not impose varying weights for each dimension. In Sarma (2015), dimensional weights are set at arbitrary values due to the lack of available data to fully characterize availability and usage dimensions. For example, the weights for access, availability, and usage are 1, 0.5, and 0.5, respectively. Camara and Tuesta (2014) use two-stage principal component analysis, wherein, in the first stage, they estimate three subindices usage, access, and barriers which define their financial inclusion measure. In the second stage, they estimate the dimension weights and the overall financial inclusion index by using the dimension subindices in the first stage as explanatory variables. In effect, their financial inclusion measure is a weighted average of three dimensions, where the weights are derived from principal component analysis. While their methodology suffers from weaknesses of its own, the weights are drawn from available data, rather than relying on the researcher s discretion and potential biases. III. INDEX OF FINANCIAL INCLUSION Before investigating what influences the change in financial inclusion and assessing the impact of financial inclusion in reducing poverty and lowering income inequality across different samples of countries, we first construct our own financial inclusion indicator. The motivation for constructing our own financial inclusion indicator are as follows: (i) we aim to include as many economies in our sample, as using a previously computed indicator will limit our sample size, which could bias results for a crosscountry setting; (ii) need exists to develop a consistent and robust measure of financial inclusion for a large sample of economies, which helps standardize the measure for all countries in our sample; and (iii) we can use this consistent and robust financial inclusion index to validate earlier findings. In computing our index of financial inclusion, we combine the approaches of Sarma (2008) and Camara and Tuesta (2014). Like Sarma (2008), we use access, availability, and usage as dimensions of our financial inclusion index. 3 We compute each indictor for each dimension as: X id, xi mi M m i i (1) 3 We classify the percentage of the adult population with bank accounts as access and not as usage, in line with existing studies on financial access (Beck, Demirgüç-Kunt, and Honohan 2009; Honohan 2007, 2008; Park and Mercado 2016, 2018; and Sarma 2008, 2015).

11 Financial Inclusion: New Measurement and Cross-Country Impact Assessment 5 where x i is the actual value of indicator i, mi is the minimum value of indicator i, M i is the maximum value of dimension i. 4 X i,d is the standardized value of indicator i of dimension d. In aggregating each indicator to a dimension index, we use principal component analysis, like Camara and Tuesta (2014). We denote j (j = 1,, p) as the jth eigenvalue, subscript j refers to the number of principal components that also coincides with the number of standardized indicators p. We assume that 1 > 2 > > p and denote P k (k = 1,, p) as the kth principal component. We derive each dimension index according to the weighted averages: p j, k 1 j Pk Dd (2) p j 1 j where D d is dimension d index and P k = Xλ j. λ j represents the variance of the kth principal component (weights) and X is the indicators matrix. The weights given to each component are decreasing, so that the larger proportion of the variation in each dimension is explained by the first principal component and so on. Following Camara and Tuesta (2014), we also account for 100% of the total variation in our dimension indices to avoid discarding information that could accurately estimate the overall country financial inclusion index. Once we have the dimension indices, we run another principal component analysis to derive the dimension weights for the overall financial inclusion. As in Equation 2, p j 1 j Pki IFIi (3) p j 1 j where IFI i is the aggregate financial inclusion index for country i. P k = Xλ j. λ j represents the variance of the kth principal component (weights of each dimension) and X is the dimensions matrix. The weights given to each component are also decreasing; and we account for 100% of the total variation in our IFI. We can also represent Equation 3 as: IFI D D D (4) i 1 1, i 2 2, i 3 3, i where are the weights derived from principal component analysis and D i are the dimensions. Equation 4 states that our index of financial inclusion for our sample of 151 advanced and emerging economies is a weighted average of individual dimensions. While we follow Sarma s (2008) definition of financial inclusion, we use better and more indicators for each dimension of our financial inclusion index. For access, the indicators include the percentage of the adult population with financial accounts to total population. This indicator is a better measure of the segment of the adult population with bank accounts compared to the number of deposit accounts per adult population. We also include the proportion of the adult population with credit and debit cards as these measures complement those who have a bank account; that is, one must have a bank account before a debit and/or a credit card is issued. Our primary data source is the World Bank s Global Findex database, which is based on individual and household survey data for Following Sarma (2015), we set the minimum value for each indicator to zero.

12 6 ADB Economics Working Paper Series No. 539 and 2014, which are aggregated to a country level. For our 2014 data on access, we also include the percentage share of the adult population with a mobile money account. 5 For the availability dimension, we include the number of commercial bank branches and of ATMs per 100,000 adults, also taken from the Global Findex database. For the usage dimension, we include the share of the adult population who borrowed and saved from a financial institution, taken from the same database. We also include the domestic credit-to-gdp ratio, sourced from the World Bank s World Development Indicators. 6 Table 1 presents the computed normalized weights for each indicator. Several observations are notable. First, changing the number of indicators in a dimension index significantly alters the resulting weights. For the access dimension, the inclusion of a mobile money account has altered the weights for 2014 for countries with available mobile account data. For those without mobile account data, the weights are like Second, the weight of commercial bank branches is significantly larger than the weight of ATMs per adult population for the availability dimension. Third, the share of those who borrow from a financial institution is far greater than the share of those who saved and for the credit-to-gdp ratio. Fourth, weights appear to be stable in both survey periods. This offers support for using principal component analysis to generate indicator and dimension weights in aggregating a financial inclusion index. Lastly, dimensional weights appear stable across the sample periods. Among the dimensions, availability appears to have greater importance than access and usage. This validates the findings of Demirgüç-Kunt and Klapper (2012), in which they find that distance or the lack of available bank branches in remote areas are primary reasons that survey respondents are involuntarily excluded from financial services. 7 Applying equations 1 to 4 on the list of indicators, Table 2 presents our cross-country IFI ordered from highest to lowest in In principle, the IFI index could reach 100, suggesting a very high level of financial inclusion. But our computed index reaches only up to for Luxembourg in This could be attributed to the use of weighted averages for our indicators and dimensions, as weighted averages make it less likely for a country to score high points on each weight. Nonetheless, the ordering of economies based on IFI appears robust such that economies like Japan, Luxembourg, Spain, and the United States always score high on previous IFI rankings, as in Sarma (2008) and Park and Mercado (2018). Figure 1 compares our new index with Sarma s (2015) index. 9 Based on the figure, our new measure is positively correlated with Sarma s index, suggesting that those economies that score high on our measure also have high financial inclusion in Sarma s (2015) index. Figure 2 illustrates IFI median values by country income groupings. As expected, high-income countries (as classified by the World Bank) score high on our IFI measure, while low-income countries score the lowest. It also shows that financial inclusion has increased overall across income groups between the sample periods The appendix lists data definitions and sources. We explored the option of including point-of-sales data from the Global Findex database. Data is available for 78 economies and the survey period is not specified. Table 1 also presents the weights of each indicator and dimension using country income-group samples. Note that similar patterns hold as in the full sample weights. We tested for the significance of each indicator on each dimension as well as the significance of each dimension on overall financial inclusion index. The regression results show all indicators and dimensions indices are significant. The estimates imply that all the indicators are relevant for each of the dimensions and that dimensions are significant for the aggregate financial inclusion index. The financial inclusion index of Camara and Tuesta (2014) is unavailable.

13 Financial Inclusion: New Measurement and Cross-Country Impact Assessment 9 7 Table 1: Principal Component Analysis of Indicators for Each Index of Financial Inclusion Dimension Full Sample HIC MHI MLI LIC Account (% age 15+) Credit card (% age 15+) Debit card (% age 15+) Mobile money (% age 15+) Branches per 100,000 pop ATMs per 100,000 pop Borrower (% age 15+) Saver (% age 15+) Credit (% GDP) Dimension 1 (Access) Dimension 2 (Availability) Dimension 3 (Usage) GDP = gross domestic product, HIC = high-income countries, LIC = low-income countries, MHI = middle-high-income countries, MLI = middle-low-income countries. Notes: Weights are normalized. Refer to the appendix for data definition and sources. Source: Authors calculations.

14 8 ADB Economics Working Paper Series No. 539 IV. EMPIRICAL METHODOLOGY AND RESULTS To address the research questions of the paper, we ran two regression models. First, we tested the covariation between the change or increase in IFI between 2011 and 2014 with average GDP growth in 2011 to 2013, average domestic credit provided by the financial sector to GDP in 2011 to 2013 as proxy for financial sector development, and average level of technology in 2011 to Specifically, we run the regression equation: IFI ' X D (5) , i , i i i Rank Economy Code Table 2: Index of Financial Inclusion Ranking, 2014 Income Group Geographic Group IFI 2011 IFI Luxembourg LUX HIC EUR Spain SPA HIC EUR United States USA HIC NAM Canada CAN HIC NAM New Zealand NZL HIC EAP Australia AUS HIC EAP Japan JPN HIC EAP United Kingdom UKG HIC EUR Korea, Republic of KOR HIC EAP Switzerland SWI HIC EUR Mongolia MON MIL EAP Israel ISR HIC MENA Norway NOR HIC EUR Sweden SWE HIC EUR Denmark DEN HIC EUR Portugal POR HIC EUR France FRA HIC EUR Italy ITA HIC EUR Hong Kong, China HKG HIC EAP Germany GER HIC EUR Croatia HRV MIH CEE Ireland IRE HIC EUR Finland FIN HIC EUR Malta MLT HIC EUR Slovenia SVN HIC CEE Austria AUT HIC EUR Cyprus CYP HIC EUR The larger the domestic credit provided by the financial sector, the deeper the financial system, as it captures not only credit to households and nonfinancial corporations, but also credit to other financial corporations and government. Technology, such as the internet, smart cards, and the use of mobile phones, can help broaden financial access, but it does not necessarily address the underlying distortions limiting access (Claessens 2006).

15 Financial Inclusion: New Measurement and Cross-Country Impact Assessment 9 Rank Economy Code Income Group Geographic Group IFI 2011 IFI The Netherlands NET HIC EUR Bulgaria BGR MIH CEE Belgium BEL HIC EUR Russian Federation RUS MIH FSU Iran IRN MIH MENA Estonia EST HIC CEE Slovakia SVK HIC CEE Latvia LVA HIC CEE Singapore SIN HIC EAP Poland POL HIC CEE Montenegro MNE MIH EUR Czech Republic CZE HIC CEE Greece GRC HIC EUR Mauritius MUS MIH SSA United Arab Emirates ARE HIC MENA Thailand THA MIH EAP Colombia COL MIH LAC Kuwait KWT HIC MENA Brazil BRA MIH LAC Macedonia MKD MIH CEE Costa Rica CRI MIH LAC Serbia SRB MIH CEE South Africa ZAF MIH SSA Malaysia MAL MIH EAP China, People s Republic of PRC MIH EAP Chile CHL HIC LAC Turkey TUR MIH MENA Qatar QAT HIC MENA Lithuania LTU HIC CEE Trinidad and Tobago TTO HIC LAC Bosnia and Herzegovina BIH MIH CEE Oman OMN HIC MENA Georgia GEO MIL FSU Romania ROU MIH CEE Hungary HUN HIC CEE Lebanon LBN MIH MENA Saudi Arabia SAU HIC MENA Kenya KEN MIL SSA Belize BLZ MIH LAC Uruguay URY HIC LAC Panama PAN MIH LAC Sri Lanka SRI MIL SAS Guatemala GTM MIL LAC

16 10 ADB Economics Working Paper Series No. 539 Rank Economy Code Income Group Geographic Group IFI 2011 IFI Namibia NAM MIH SSA Ukraine UKR MIL FSU Bolivia BOL MIL LAC Kosovo UVK MIL CEE Belarus BLR MIH FSU Dominican Republic DOM MIH LAC Botswana BWA MIH SSA Venezuela VEN MIH LAC Jamaica JAM MIH LAC Kazakhstan KAZ MIH FSU Indonesia INO MIL EAP Armenia ARM MIL FSU Ecuador ECU MIH LAC Argentina ARG MIH LAC Albania ALB MIH CEE Mexico MEX MIH LAC Morocco MAR MIL MENA El Salvador SLV MIL LAC Uzbekistan UZB MIL FSU Honduras HND MIL LAC Azerbaijan AZE MIH FSU Viet Nam VIE MIL EAP Cambodia CAM MIL EAP Tunisia TUN MIL MENA Uganda UGA LIC SSA Jordan JOR MIL MENA India IND MIL SAS Peru PER MIH LAC Swaziland SWZ MIL SSA Bhutan BHU MIL SAS Paraguay PRY MIH LAC Philippines PHI MIL EAP Lao People s Democratic Rep. LAO MIL EAP Nigeria NGA MIL SSA Nepal NEP LIC SAS Rwanda RWA LIC SSA Moldova MDA MIL CEE Ghana GHA MIL SSA Nicaragua NIC MIL LAC Zimbabwe ZWE LIC SSA Angola AGO MIL SSA Gabon GAB MIH SSA Algeria DZA MIH MENA

17 Financial Inclusion: New Measurement and Cross-Country Impact Assessment 11 Rank Economy Code Income Group Geographic Group IFI 2011 IFI Kyrgyz Republic KGZ MIL FSU Tanzania TZA LIC SSA Zambia ZMB MIL SSA Bangladesh BAN MIL SAS West Bank and Gaza PSE MIL MENA Mauritania MRT MIL SSA Syria SYR MIL MENA Myanmar MYA MIL EAP Cote d'ivoire CIV MIL SSA Comoros COM LIC SSA Liberia LBR LIC SSA Egypt EGY MIL MENA Lesotho LSO MIL SSA Benin BEN LIC SSA Djibouti DJI MIL MENA Malawi MWI LIC SSA Senegal SEN LIC SSA Mali MLI LIC SSA Ethiopia ETH LIC SSA Congo Republic COG MIL SSA Pakistan PAK MIL SAS Togo TGO LIC SSA Haiti HTI LIC LAC Burkina Faso BFA LIC SSA Sudan SDN MIL SSA Tajikistan TAJ MIL FSU Sierra Leone SLE LIC SSA Iraq IRQ MIH MENA Congo Democratic Republic COD LIC SSA Cameroon CMR MIL SSA Chad TCD LIC SSA Afghanistan AFG LIC SAS Madagascar MDG LIC SSA Guinea GIN LIC SSA Burundi BDI LIC SSA Niger NER LIC SSA Yemen YEM MIL MENA Central African Republic CAF LIC SSA HIC = high-income countries, LIC = low-income countries, MHI = middle-high-income countries, MLI = middle-low- income countries. Notes: Ranking based on 2014 Index of Financial Inclusion (IFI) values. Refer to section II for the discussion of the construction of the IFI. Hong Kong, China, which is a special administrative region of the People s Republic of China, is classified as a high-income country for purposes of this research. Source: Authors calculations.

18 12 ADB Economics Working Paper Series No. 539 Figure 1: Index of Financial Inclusion and Sarma Measure (2015) Sarma IFI SWI JPN POR MLTKOR UKG FRA GRCBEL RUS FIN IRE TUR MAL MUS EST BGR NET UKR CHL LVA CZE KEN MKD MNE GER JAM LBN AUT THA TZA PANTTO CRI MDA PER GTM ZAF HRV ITA IND HUN BRA MON INO VEN UVK BAN SWZ RWA BHU MEXGEO BLZ ARE ARM ARG AZE BIH ECU MARNAM PSE JOR HND SAU LBR SLV BWA UGA ZWE LSO PHI BOL DOM PAK NEP DZA ZMB CAM GHAAGO PRC MDGEGY QAT GINCMR SEN COM CIV SYR NIC MLI MWI DJI GAB NGA PRY BDI AFG BFA MYA MRT YEM NER COG LAO CAFTCD SPA Level_IFI1114 Fitted values Sarma_IFI IFI = Index of Financial Inclusion. Notes: Level_IFI1114 pertains to the average IFI for 2011 and Sarma (2015) values refer to the average values for 2011 and Refer to Table 2 for the definition of the codes. Source: Authors calculations. where X i is the row vector of regressors and D i is a dummy variable for membership in the Alliance for Financial Inclusion. 11 We estimate Equation 5 to determine whether growth rate, technology, size of financial market (proxied by domestic credit provided by the financial sector), and membership in the financial inclusion alliance significantly covary with the change of financial inclusion for the full sample and individual country income groups. 12 This allows us to assess whether the determinants remain relevant in explaining covariation with the change in financial inclusion across income groups. Figure 3 illustrates the relationship between the GDP growth rate in and the change in financial inclusion between sample periods. We observe an upward sloping scatter plot line, implying that economies with high average growth rates in tend to have increased financial access For the Alliance for Financial Inclusion member central banks and monetary authorities, see We use the proportion of population that accessed the internet in the past 3 months as our technology measure, since it reflects information and communication technology, which aids financial access, following the discussion of Claessens (2006).

19 Financial Inclusion: New Measurement and Cross-Country Impact Assessment 13 Figure 2: Index of Financial Inclusion by Income Group (Median) LIC MIL MIH HIC IFI 2011 IFI 2014 HIC = high-income economies, IFI = Index of Financial Inclusion, LIC = low-income economies, MIH = middle-income high economies, MIL = middle-income low economies. Note: Median values based on the Index of Financial Inclusion presented in Table 3. Source: Authors calculations. Second, we test the covariation between the average level of financial inclusion in and the level of poverty, income inequality, entrepreneurship, and female empowerment in We expect economies with higher financial inclusion would have lower poverty rates and income inequality and higher entrepreneurship (Dupas and Robinson 2009) and female empowerment (Ashraf, Karlan, and Yin 2010). Figure 4 demonstrates this negative relationship between the average level of IFI in and headline poverty rates in Figure 5 also exhibits this pattern for income inequality. Figure 6 shows a strong positive correlation between our financial inclusion measure and rule of law, although the correlation may be spurious. To formally test the covariation, we estimate the regression equation: Y IFI ' X ' D ' D * IFI (6) , i , i , i i i , i i where Y pertains to the average values of poverty, income inequality, entrepreneurship, and female empowerment for IFI is the average value for financial inclusion in 2011 and X is a row vector of regressors which includes the average values of secondary education completion and GDP growth rates for D is a dummy variable for country income groupings. D*IFI is the interaction term between the country income group and financial inclusion. 14 The interaction term in Equation 6 will indicate whether financial inclusion for a specific income group exerts more or less significant impact on poverty and income inequality than other income groups We considered including productivity as one of our explanatory variables. However, any changes in productivity are captured by the average GDP growth rate. Refer to Table 3 for the country income groups, and the appendix for the full list of data notes and sources.

20 14 ADB Economics Working Paper Series No. 539 Figure 3: Change in Financial Inclusion on Gross Domestic Product Growth Change_IFI CAF GRC SDN SLV DZA UGAB KEN INO NERCAM BWA MLI KGZ TZA SEN COG NGA BENGEO SAU BFA MDG NIC RUS CIV EGY ISRURYMRT BOL PRC GHA ITA AZEGIN JPN PAK HND POLVEN BGR ZAF CRI CHL INDRWA TGO ZMB ARE PAN MEX TAJUZB MKD MAL HRV MNE UKG UKR CZE GER SVK ARG JORDOM NEP BRAGTM UVK ARM MDA USA CAN VIE PHI KAZ YEM ECU HKG SPA BEL LUX NZL AUS FRA SWE AUT LBN ROU MAR PER LTU SIN SRI TUR JAM BIH TTO ALB SWI KOR MUS LVA LSO PSE BHU TUN EST PRY QAT IRN IRE BLZ BLR COMBDI DJI FIN POR DEN SRB NOR MLT THASWZ OMN NAM LAO LBR MYA ZWE SVN NET HUN ETH MWI CMR HTI AGO TCD BAN AFG Growth1113 IRQ MON SLE Fitted values DIFI Notes: Change refers to the difference of IFI log values in 2011 and Growth refers to the average gross domestic product growth rate for 2011 to Refer to Table 2 for the definition of the codes. Source: Authors calculations. Figure 4: Poverty on Index of Financial Inclusion Poverty_ BDI TGO ZMB YEM BEN CIV NER HND RWA MEX GTM ZAF AFG BFA BOL CMR LBR DOM SLV TAJ MRT KGZ VEN PAK ARG ARM EGY NIC IND PRY NEP SRB BAN ECU PHI LAO PER PAN GEO LTU CRI MKD LVABGR MON MYA UVK HRV ISR BIH TUR POL USA HUN GRC EST SPA CAM TUN VIE ITASVN PORKOR BHUUZB CHL RUS INO UKG AUS MDA THA SWI NZL CAN URY SVK BELAUTGER IRE BRA NET FRA SWE NOR UKRSRI PRC CZE FIN AZE BLR DEN KAZ MAL LUX Level_IFI1114 Fitted values Pov1416 Notes: Level_IFI1114 pertains to the average IFI for 2011 and Poverty refers to the average value of poverty headcount ratio for 2014 to Refer to Table 2 for the definition of the codes. Source: Authors calculations.

21 Financial Inclusion: New Measurement and Cross-Country Impact Assessment 15 Figure 5: Income Inequality on Index of Financial Inclusion Income inequality_ ZMB HND PAN BRA PRY GTM CRI BEN MEX CHL CMR NIC BOL ECU MAL PER DOM TGO ARG CIV PRC SLV URY TUR PHI BHU INO GEO SRI RUS IRN USA MYA LAO SRB LTU THA BGR YEM ROUMKD GRC ISR POR SPA BFA IND VIE LVA EST ITA NZL NER UKG LBR AUS TAJ NEP EGY MRT BAN ARM POL MNE HRVIREMON FRA HUN CAN PAK CAM AUT KOR SWI NET GER KGZ BEL MLT DEN MDA FIN SWE NOR KAZ BLR CZESVK SVN UKR LUX Level_IFI1114 Fitted values Gini1416 Notes: Level_IFI1114 pertains to the average IFI for 2011 and Income inequality refers to the average value of GINI coefficient for 2014 to Refer to Table 2 for the definition of the codes. Source: Authors calculations. For both Equations 5 and 6, we limit the number of regressors included to avoid multicollinearity among regressors. 15 We also use robust standard errors to address potential heteroskedasticity. In addition, regressing the dependent and independent variables in two different time periods reduces endogeneity, that is, the explanatory variables are lagged. This empirical approach is recommended to address endogeneity in a cross-section regression without the need to use an instrument variable. Finally, we estimated Equations 5 and 6 using ordinary least squares estimation. We do not use an instrument variable with two-stage least squares estimation because a valid instrument variable is lacking. Using a weak instrument variable would lead to inefficient estimation, as the standard errors in the second-stage regression will be higher, yielding inconsistent results We run similar estimation including rule of law. However, we find that it is highly correlated with financial inclusion, such that the unconditional correlation between rule of law and financial inclusion is significant at This leads to multicollinearity when both rule of law and financial inclusion are included in the empirical specification. Hence, we opted to drop rule of law in our current specification. We considered applying a randomized experiment approach using membership in the Alliance for Financial Inclusion as treatment. But given that our sample is a highly heterogeneous group with varying income levels, and the sample is small, we could not apply it.

22 16 ADB Economics Working Paper Series No. 539 Figure 6: Rule of Law on Index of Financial Inclusion Rule of law FIN SWENOR AUT NET DNK NZL SIN IRE SWI AUS UKG CAN GER HKG USA CHL BEL JPN FRA EST MLT CZE SVN POR SPA ISR KOR QAT MUS LTU POL LVA BWA URY HUN OMN ARE MAL CRI GRC SVK JOR ITA BHU ZAF HRV SAU NAM GHA ROU TUR BRA MNE IND GEO TUN PAN THA BGR SRI BIH TTO LSO MWI RWA MAR MKD SEN ZMBMDA UGA SRB MON BFA PSE ARM ALB JAM EGYGAB BLZ TZA PHI SWZ UVK PRC NER VIE PERINO MEX MLI BEN ETH NIC SLV ARG KAZ DOM DZA LBN DJI NEP KEN UKR RUS BGD PRY AZE MDGPAK SLE TGO LBR LAO COM MRT BOL BLR IRN BDI CMRCIVSYR CAM KGZ HND ECU GTM COG NGA YEMSDN TAJ AGO HTI MYA UZB CAF GIN TCD IRQ AFG ZWE VEN LUX Level_IFI1114 Fitted values Rule1113 Notes: Level_IFI1114 pertains to the average IFI for 2011 and Rule of law is the average values of the percentile ranking for Data on rule of law are taken from the World Governance Indicators. Refer to Table 2 for the definition of the codes. Source: Authors calculations. Table 3 presents the estimates for Equation 5 on the covariation between the change in financial inclusion and growth, credit, financial sector development, and technology. We do not find significant cross-country covariation between the change in financial inclusion and growth, credit, financial sector development, and technology for the full sample estimates in column (1). But splitting the country income groups into high, middle-high, middle-low, and low-income countries as classified by the World Bank yields some interesting results. The results show that, for high- (column 2) and middle-high (column 3) income economies, higher output growth significantly covaries with higher financial inclusion. However, we do not see the same results for middle-low (column 4) and low-income (column 5) economies. In fact, for both groups, output growth has a negative sign, albeit insignificant. The results indicate that greater financial inclusion significantly covaries with higher output growth only for high- and middle-high-income economies and not for middle-low and lowincome economies. This implies the presence of nonlinear effects of economic growth on financial inclusion. In low-income economies, economic growth has no significant effect on financial inclusion. But economic growth can positively influence the degree of financial inclusion in higherincome economies, which might reflect better institutional quality in these economies to allow better access to finance. Tables 4 8 assess the impact of financial inclusion on poverty, income inequality, entrepreneurship, and female empowerment. Apart from poverty and inequality, we added entrepreneurship and female empowerment, as financial inclusion is often discussed as a key driver for

23 Financial Inclusion: New Measurement and Cross-Country Impact Assessment 17 these two important economic variables in micro-level studies using individual and household survey data. Table 4 presents evidence that economies with higher financial inclusion have significantly lower poverty. However, we do not find similar results for income inequality, entrepreneurship, and female empowerment. We also find that educational attainment significantly covaries with lower poverty, less income inequality, but with less entrepreneurship; and higher output growth significantly covaries with higher income inequality and entrepreneurship. Table 3: Estimates on the Change in Index of Financial Inclusion, by Income Group (1) (2) (3) (4) (5) Variables Change in IFI Countries All Countries High Income Middle-High Middle-Low Low Income Income Income Growth *** 0.018** [1.000] [3.089] [2.497] [ 0.083] [ 0.483] Financial Sector Development * [ 1.114] [1.271] [ 1.886] [ 0.040] [0.247] Technology [0.581] [ 1.611] [0.793] [0.590] [0.756] AFI Member [1.461] [1.208] [0.038] [ 0.372] [1.324] Constant [1.198] [1.309] [0.579] [0.279] [ 0.402] Observations R-squared AFI = Alliance for Financial Inclusion, IFI = Index of Financial Inclusion. Notes: Dependent variable is the change in IFI from 2011 to Refer to the appendix for definition and data sources of Growth, Credit, Technology, and AFI membership. Refer to Table 2 for the list of economies included in each income group. t-stats are reported in brackets. *** p<0.01, ** p<0.05, * p<0.1. Robust standard errors are used. Source: Authors estimates. Tables 5 and 6 present the results when we split the sample into high- and middle-high and middle-low and low-income economies 17. For high- and middle-high-income economies, in Table 5, we find that higher financial access significantly covaries with lower poverty rates, while educational attainment significantly covaries with lower poverty rates, income inequality, but lower entrepreneurship. In contrast, in Table 6, we do not find financial inclusion to be significant for middlelow and low-income economies. Nonetheless, we find higher educational attainment to significantly covary with lower poverty rates for the subset of economies. Tables 5 and 6 provide evidence from high- and middle-high-income economies that financial inclusion significantly lowers poverty. It could suggest that middle-low and low-income economies may have other features that impede the effect of financial inclusion on poverty and income inequality. 18 Exploring interaction effects between financial inclusion and other factors might be worthwhile in assessing whether financial inclusion alone is a sufficient factor in lowering poverty and income inequality. This is because, as suggested by Beck, We combined high-and middle-high-income and middle-low and low-income economies in both regressions to have sufficient sample size to assume normality of both samples. Kenya would be a case in point. It has relatively high financial inclusion given the widespread use of mobile money. However, poverty remains high, perhaps due to other factors, or the impact of financial inclusion on poverty might take time to be reflected on an aggregate level.